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arXiv Machine Learning By Joshua Dimasaka, Christian Gei{\ss}, Emily So

DeepC4: Deep Conditional Census-Constrained Clustering for Large-scale Multitask Spatial Disaggregation of Urban Morphology

Read the original on arXiv Machine Learning →

DeepC4 is a deep learning-based spatial disaggregation method that uses local census statistics as cluster-level constraints and incorporates multiple conditional label relationships in a multitask learning framework. Applied to Rwandan urban morphology, it achieves macro‑F1 scores of 0.63, 0.78, and 0.45 for roof, wall, and height prediction, respectively, and estimates national dwelling and occupant counts within about 1.1% error compared to census records. The approach outperforms existing GEM and METEOR methods and covers 32‑49% more 500‑meter grid pixels across provinces.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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